Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting

TL;DR

This paper introduces the Diagnosis of Thought (DoT) prompting method, leveraging large language models to detect cognitive distortions in psychotherapy, aiming to assist professionals amid mental health care accessibility challenges.

Contribution

It proposes a novel three-stage diagnosis framework using LLMs for cognitive distortion detection, improving accuracy and rationale quality over existing methods.

Findings

DoT outperforms ChatGPT in cognitive distortion detection accuracy.

Generated rationales are validated as high-quality by human experts.

The approach enhances AI-assisted psychotherapy tools.

Abstract

Mental illness remains one of the most critical public health issues of our time, due to the severe scarcity and accessibility limit of professionals. Psychotherapy requires high-level expertise to conduct deep, complex reasoning and analysis on the cognition modeling of the patients. In the era of Large Language Models, we believe it is the right time to develop AI assistance for computational psychotherapy. We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. The generated diagnosis rationales through the three stages are essential for assisting the professionals. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.